Maintenance is a strategic concern when developing & manufacturing a product – and for good reasons. A third of all maintenance activities are carried out too frequently – and, according to IBM, nearly half are ineffective. The article attempts to provide a comprehensive view regarding the advent of Industry 4.0 and predictive maintenance becoming the top business objective for many manufacturers – and how to overcome the challenges of implementing such a system.

For machine operators and factory managers, preventative maintenance and asset repairs consume unnecessary resources, and eat deeply into operational costs presenting serious impediments to efficient operations. A single hour of downtime alone can cost a large enterprise over $100,000 in lost productivity and can be a hard hit to customer satisfaction. For product manufacturers, this means higher field service costs, higher customer service centre costs, lower customer satisfaction and a distinct disadvantage facing the competition.

Manufacturers and asset managers are turning to Industry 4.0, also known as the Industrial Internet of Things (IIoT), for a superior approach. This involves continually generating and transmitting product behaviour data, capturing the data in a central repository and applying advanced Big Data analytics techniques to sort through massive amounts of data and identify important patterns. This pattern identification can lead to ‘just-in-time maintenance’ - actionable insights that predict product failure to increase product uptime and improve asset efficiency. Given this, it’s no surprise that the market for predictive maintenance applications is surging; it is predicted by one report to hit $10.9B by 2022.

Benefits of predictive maintenance

Imagine if you received an alert from a mobile app ahead of any fault occurring. Instead of having to guesstimate when the part will be obsolete based on past observations or hope to catch it through regular monitoring; predictive analytics tell you when to replace the part, reducing planned downtime and keeping the product running for an optimum amount of time. Predictive maintenance also eliminates unnecessary repair costs, a large unknown for both, manufacturers and end users. When an electronic component in a product fails, identifying the problem may take 5 minutes – or 5 hours. The same holds true for replacing broken or worn-down parts.

Major breakdowns are expensive, both because of lost operating time as well as secondary financial losses. Worse, the larger or more complex the machinery, greater the impact maintenance has on production and runtime costs. Even a small flaw in the system, if not caught early, can lead to unexpected and costly downtime. With preventative maintenance, replacing parts too early also carries an unnecessary financial burden on the business.

That’s where predictive maintenance & Industry 4.0 steps in:

Reduce downtime and improve production yield – Reduce unplanned downtime by catching issues before they can make the whole system fail. And reducing planned manual inspections also boosts productivity and production yield.

Monetise predictive maintenance – When manufacturers can prove they have increased uptime and lowered maintenance costs, they can deliver a measure of predictability to their customers that can increase purchase price and be leveraged as a strategic competitive edge. The opportunity to introduce digital services to customers based on data analytics can also generate a recurring revenue stream and breakthrough growth for the company.

Improve customer satisfaction – Automated alerts that remind customers when it’s time to replace parts and recommend maintenance services at specific times will both differentiate your product from others in market and keep customers happy

To deliver these objectives, manufacturers turn to Industry 4.0 - the interconnected devices, sensors, cloud, gateways and other parts of Industrial Internet of Things system in order to quickly collect massive quantities of data from multiple machines and locations, and then apply Big Data analytics to deliver predictive maintenance insights.

The evolution of predictive maintenance

Manufacturers have been carrying out predictive maintenance for years, with different levels of maintenance activities corresponding to the company’s level of maturity:

Reactive maintenance – This deals with problems after they arise, i.e. ‘fighting fires’

Preventative maintenance – This involves visual inspections, followed by regular asset inspections that provide more specific, objective information about the condition of the machine or system

Rule-based predictive maintenance – Also known as ‘condition monitoring’. Sensors continuously collect data about assets and send alerts according to predefined rules, including when a predefined threshold has been reached.

Machine learning-based predictive maintenance – This relies on large sets of historical or test data, combined with tailored machine-learning algorithms, to run different scenarios and predict what will go wrong, and when - and then generate alerts

Rule-based predictive maintenance

Every company with products in the market can identify some reasons for equipment failure. Product teams can confer with engineering and customer service departments to establish known causes for machine breakdown or learn which situations have a high likelihood of leading to parts failure. With the common reasons for product failure established, product teams then set to define the IoT model – a blueprint of the connected system of data-collecting sensors, data connectivity, applications, cloud, gateway and other system components.

The model also defines the product use cases, with ‘if-this-then-that’ rules which describe the behaviours and interdependencies between the various IoT system components For example, if temperature and rotation speed are above certain predefined levels, the system will send an alert to a web dashboard or personal app, so that the danger can be addressed in time.

This IoT model provides much-needed clarity to data gathering from the get-go. With all its benefits, data collection is only useful when the right data is collected, the collection is controlled and the right business decisions are made in response to that data. One can use simulation to validate the model use cases - including data simulation to validate the dashboards and alerts to ensure that the necessary data is collected. This helps teams catch errors early on, before spending a penny on development and production.

Simulating how the smart machine, product, or system will function in ‘real life’, helps teams catch errors early on - before spending a penny on development and production. Later, the same dashboard can be integrated with insights from machine learning to provide a visually understandable heatmap of asset conditions in real-time.

With the validated model in place, we can proceed to delivery. An IoT development platform can be used to enable collaboration between internal and external teams and keep the model in sync with the developed product at all times. One’s IoT development platform should also (automatically) translate one’s IoT model into predictive maintenance dashboards and alerts in accordance with the use cases defined in the model.

Machine learning predictive maintenance

Machine learning is crucial for acting on insight, rather than hindsight. When properly designed and implemented, a machine learning algorithm will learn one’s normal data’s behaviour and identify deviation in real-time.

Data collection – Sensors implemented in machines to gather data on the machine and its environment

Feature extraction and reduction – Elements for measuring are chosen and extracted; for example, temperature levels or motor rotation speed

Model creation – An algorithm runs all the data multiple times in a learning model

Model validation – The model with its data is tested against the real-world variables or ‘output’ that manufacturers are testing for; for example, alarms when the system stops working

Deployment – The model is deployed, and if it shows anything but the desired behaviour, the discrepant behaviour is relayed back to the model to improve the system’s future performance

This system requires both input (historical and a training set data) along with output (the desired result). A machine monitoring system would include input on different temperatures, engine speed, etc. and the output would be the variable in question - a warning of future system or parts failure. The system will then be able to predict when a breakdown will likely occur.

Challenges in implementing machine learning for Industry 4.0

We’ve established that predictive maintenance provides tremendous business benefits, and that machine learning is an advanced approach to implement predictive maintenance. Yet, according to a survey by PwC, only 11% of surveyed companies have ‘achieved’ machine-learning-based predictive maintenance.

There are three primary challenges in implementing machine learning for predictive maintenance:

Identifying the necessary data to collect: Manufacturers who have never launched a connected system or machine begin blindfolded - without clear evidence of which data will prove valuable, or which data to collect. Anomaly detection, and predictive analytics as a whole, cannot be an afterthought; it must be implemented in business goals and planning from the beginning.

Obtaining the necessary data set: Without this input, it’s impossible to start running a machine-based algorithm. Time is not on one’s side - it takes a significant amount of time and resources to develop a machine learning solution or select the right algorithm and choosing the wrong oneleads to massive losses in costs and deadlines.

Advanced data science: Understanding a pile of often messy historical data requires not only the right algorithms - there are dozens of possible machine learning algorithms to choose from - but also a method of presenting the data-driven insight clearly after it has been analysed. If the

Due to these challenges, predictive maintenance is often restricted to the minority of companies whose machines have been collecting data for years, and now utilise advanced analytics platforms to sort through that data. Companies implementing a connected system for the first time, with the goal of implementing machine learning for predictive maintenance, will find it difficult to cross the starting line.

Getting started – a pragmatic approach

A pragmatic approach to bypass the need for a large historical data set, and advanced machine learning algorithms at the very outset, is to begin with rule-based predictive maintenance. This gives companies quick business results and a steppingstone into machine learning. Instead of requiring large data sets ahead of time, one can start with basic assumptions or ‘rules’. Over time, as the system aggregates historical data the company can experiment with and apply machine learning algorithms for more accurate predictions. Because the predictive, rule-based model can be designed without live sensors or historical data sets, it democratises predictive maintenance - making it viable both, for companies who have already implemented IoT and for those who haven’t yet designed their connected system.

Rule-based predictive maintenance is not the be-all and end-all for predictive maintenance. The approach is based on ‘what if’ scenarios that one can define, rather than a machine algorithm running possible scenarios. It’s not fool-proof, but it is achievable, affordable and delivers business benefits. The ideal solution is to start with a rule-based model, defining and simulating use cases and required alerts. Once one has collected enough data, one should implement machine learning algorithms and iteratively refine them based on the accuracy of the predictions attained.

Courtesy: SeeboWhy predictivemaintenanceis drivingIndustry 4.0

Image Gallery

Predictive analytics tell you when to replace the part, reducing planned downtime and keeping the product running for an optimum amount of time

IoT development platform can be used to enable collaboration between internal and external teams and keep the model in sync with the developed product at all times

The standard machine learning process will be able to predict when a breakdown will likely occur